Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: categorizing a plurality of data blocks into a plurality of categories according to an access pattern and a size corresponding to respective data blocks of the plurality of data blocks, wherein each category of the plurality of categories is associated with a respective access pattern level and respective data block storage size requirement; accessing device characteristic information for each storage device of a plurality of storage devices; determining a total number of data blocks in a first category; summing the device characteristic information to determine a total value of the plurality of storage devices; determining a proportional value for the first category for each of the plurality of storage devices by dividing the device characteristic value of each respective storage device by the total value; calculating a target number of data blocks for each storage device of the plurality of storage devices by multiplying each respective storage device's proportional value by the total number of data blocks in the first category, each respective storage device's calculated target number proportional to the respective storage device's device characteristic information; and redistributing the data blocks in the first category across the plurality of storage devices according to the calculated target number of data blocks for each of the plurality of storage devices.
This invention relates to data storage systems and addresses the challenge of efficiently distributing data blocks across multiple storage devices based on access patterns and storage characteristics. The method categorizes data blocks into multiple categories according to their access patterns and sizes, where each category is defined by a specific access pattern level and storage size requirement. Device characteristics, such as capacity or performance metrics, are collected for each storage device in the system. For a selected category, the total number of data blocks is determined, and the device characteristics are summed to compute a total value across all storage devices. A proportional value is then calculated for each storage device by dividing its individual characteristic value by the total value. This proportional value is multiplied by the total number of data blocks in the category to derive a target number of data blocks for each storage device, ensuring distribution aligns with device capabilities. Finally, the data blocks in the category are redistributed across the storage devices according to these calculated targets, optimizing storage utilization and performance. This approach ensures balanced and efficient data placement based on both access patterns and storage device characteristics.
2. The method of claim 1 , wherein the device characteristic comprises a determined performance characteristic.
A method for evaluating device performance involves analyzing a device characteristic, specifically a determined performance characteristic, to assess the operational capabilities of the device. The performance characteristic may include metrics such as processing speed, power consumption, reliability, or efficiency, which are measured or calculated to provide insights into the device's functionality. This evaluation helps identify potential improvements or optimizations for the device, ensuring it meets desired operational standards. The method may involve comparing the determined performance characteristic against predefined benchmarks or historical data to determine whether the device is functioning within acceptable parameters. By focusing on performance characteristics, the method enables targeted adjustments or maintenance to enhance device reliability and efficiency. The analysis may be conducted using diagnostic tools or software that monitor and record performance data over time, allowing for continuous assessment and refinement of the device's operational state. This approach is particularly useful in industries where device performance directly impacts productivity, such as manufacturing, telecommunications, or computing. The method ensures that devices operate at peak efficiency, reducing downtime and maintenance costs while extending the device's lifespan.
3. The method of claim 1 , wherein the device characteristic comprises a storage device's bandwidth.
A method for optimizing data storage performance in a computing system addresses the challenge of inefficient resource allocation in storage devices. The method involves monitoring and dynamically adjusting storage device characteristics to improve performance. Specifically, the method measures the bandwidth of a storage device, which refers to the rate at which data can be read from or written to the device. By analyzing this bandwidth, the system can identify bottlenecks or underutilized capacity. The method then adjusts storage operations, such as data placement or access patterns, to balance workloads and maximize throughput. This dynamic adjustment ensures that the storage device operates at optimal efficiency, reducing latency and improving overall system performance. The method may also incorporate additional device characteristics, such as latency or error rates, to further refine performance optimization. By continuously monitoring and adapting to changing conditions, the system maintains high performance even under varying workloads. This approach is particularly useful in environments with high data throughput demands, such as data centers or cloud storage systems.
4. The method of claim 1 , wherein the device characteristic comprises a storage capacity.
A system and method for managing device characteristics in a computing environment involves monitoring and adjusting device attributes to optimize performance. The invention addresses the challenge of efficiently utilizing device resources by dynamically assessing and modifying key parameters such as storage capacity. The system includes a monitoring module that tracks device performance metrics, including storage usage, and a control module that adjusts storage allocation based on real-time data. The method involves detecting storage capacity thresholds, predicting future storage needs, and reallocating storage resources to prevent bottlenecks. By integrating predictive analytics, the system ensures optimal storage utilization, reducing downtime and improving efficiency. The invention is applicable in data centers, cloud computing, and edge devices where storage management is critical. The solution enhances scalability and reliability by dynamically adapting to changing storage demands, ensuring seamless operation across diverse computing environments.
5. The method of claim 1 , wherein a value of the device characteristic is different for at least two of the storage devices of the plurality of storage devices.
This invention relates to storage systems and methods for managing storage devices with varying characteristics. The problem addressed is the need to optimize performance, reliability, or other operational parameters in a storage system by leveraging differences in device characteristics across multiple storage devices. The method involves configuring a storage system with a plurality of storage devices, where at least two of the storage devices have different values for a specific device characteristic. This characteristic could include performance metrics (e.g., read/write speeds), reliability indicators (e.g., error rates), or other operational parameters (e.g., power consumption, capacity). By intentionally introducing or utilizing these differences, the system can achieve improved overall performance, better load balancing, or enhanced fault tolerance. For example, faster storage devices may be assigned to high-priority tasks, while slower devices handle less critical operations. Similarly, devices with higher reliability may be prioritized for critical data storage, while others are used for temporary or less important data. The method ensures that the storage system dynamically adapts to the varying capabilities of its components, optimizing resource allocation and system efficiency. This approach is particularly useful in heterogeneous storage environments, where devices from different manufacturers, generations, or performance tiers are integrated into a single system. By accounting for these differences, the method ensures that the storage system operates at its full potential while maintaining reliability and efficiency.
6. The method of claim 1 , wherein the access pattern level corresponds to an access time range.
A system and method for optimizing data storage and retrieval in a computing environment addresses the challenge of efficiently managing data access patterns to improve performance and resource utilization. The invention involves categorizing data access operations based on their temporal characteristics, such as frequency, latency, and predictability, to determine an optimal storage strategy. By analyzing these access patterns, the system dynamically adjusts storage parameters, such as caching policies, prefetching mechanisms, and data placement, to minimize latency and maximize throughput. The method includes monitoring access operations to identify recurring patterns, classifying these patterns into predefined levels, and applying corresponding storage optimizations. For example, frequently accessed data may be prioritized for faster storage media, while infrequently accessed data may be moved to lower-cost storage. The access pattern level is mapped to a specific access time range, ensuring that data retrieval times align with application requirements. This approach enhances system efficiency by reducing unnecessary data transfers and optimizing resource allocation based on real-time access behavior. The invention is particularly useful in distributed storage systems, cloud computing, and high-performance computing environments where data access patterns vary dynamically.
7. The method of claim 1 , wherein the access pattern level corresponds to an access count range.
A system and method for managing data access in a storage environment addresses the challenge of efficiently tracking and controlling data access patterns to optimize performance and security. The invention categorizes data access operations into distinct access pattern levels, each corresponding to a specific range of access counts. These levels are used to determine appropriate access control measures, such as read/write permissions, encryption, or caching strategies, based on the frequency of data access. By dynamically adjusting access policies according to these predefined levels, the system ensures that frequently accessed data is handled efficiently while less accessed data is secured appropriately. The method involves monitoring access operations, counting occurrences, and assigning the data to an access pattern level based on the count range. This approach improves storage efficiency, reduces unnecessary processing overhead, and enhances security by applying stricter controls to infrequently accessed data. The invention is particularly useful in large-scale storage systems where varying access frequencies require adaptive management strategies.
8. A non-transitory computer readable storage medium executing computer program instructions, the computer program instructions comprising instructions for: categorizing a plurality of data blocks into a plurality of categories according to an access pattern and a size corresponding to respective data blocks of the plurality of data blocks, wherein each category of the plurality of categories is associated with a respective access pattern level and respective data block storage size requirement; accessing device characteristic information for each storage device of a plurality of storage devices; determining a total number of data blocks in a first category; summing the device characteristic information to determine a total value of the plurality of storage devices; determining a proportional value for the first category for each of the plurality of storage devices by dividing the device characteristic value of each respective storage device by the total value; calculating a target number of data blocks for each storage device of the plurality of storage devices by multiplying the respective storage device's proportional value by the total number of data blocks in the first category, each respective storage device's calculated target number proportional to the respective storage device's device characteristic information; and redistributing the data blocks in the first category across the plurality of storage devices according to the calculated target number of data blocks for each of the plurality of storage devices.
The invention relates to data storage management, specifically optimizing data distribution across multiple storage devices based on access patterns and device characteristics. The problem addressed is inefficient data placement, which can lead to performance bottlenecks or underutilized storage resources. The solution involves categorizing data blocks into multiple categories based on their access patterns and sizes, where each category has an associated access pattern level and storage size requirement. For each category, the system accesses device characteristics (e.g., capacity, performance metrics) of all storage devices in a pool. It then calculates a proportional distribution of data blocks from that category across the devices, ensuring the distribution aligns with each device's characteristics. This involves summing the device characteristics to determine a total value, computing a proportional value for each device, and multiplying by the total number of blocks in the category to derive a target block count per device. The data blocks are then redistributed accordingly. This approach ensures balanced and optimized storage utilization while accommodating varying access patterns and device capabilities.
9. The medium of claim 8 , wherein the device characteristic comprises a determined performance characteristic.
A system and method for analyzing device characteristics in a computing environment involves monitoring and evaluating performance metrics of electronic devices to optimize their operation. The system collects data on various device characteristics, including hardware and software parameters, to assess their impact on performance. A performance characteristic, such as processing speed, memory usage, or energy efficiency, is determined based on the collected data. This characteristic is then used to adjust device settings, allocate resources, or trigger maintenance actions to improve overall functionality. The system may employ machine learning algorithms to predict performance degradation and recommend corrective measures. By continuously monitoring and analyzing these characteristics, the system ensures devices operate at optimal levels, reducing downtime and enhancing user experience. The approach is applicable to various devices, including servers, mobile devices, and embedded systems, and can be integrated into existing management frameworks for seamless operation.
10. The medium of claim 8 , wherein the device characteristic comprises a storage device's bandwidth.
A system and method for optimizing data storage performance in a computing environment addresses the challenge of inefficient resource allocation in storage systems. The invention monitors and analyzes device characteristics, such as storage device bandwidth, to dynamically adjust data distribution and access patterns. By evaluating bandwidth utilization, the system identifies bottlenecks and reallocates data to balance workloads across multiple storage devices. This improves overall system performance by reducing latency and maximizing throughput. The method involves continuously collecting performance metrics, comparing them against predefined thresholds, and triggering reconfiguration actions when thresholds are exceeded. The system may also predict future bandwidth demands based on historical data to proactively optimize storage operations. The invention is particularly useful in high-performance computing environments where storage bandwidth is a critical performance factor. By dynamically adapting to changing workload conditions, the system ensures efficient use of available storage resources while maintaining responsiveness. The solution integrates with existing storage management frameworks and can be applied to various storage technologies, including solid-state drives and network-attached storage systems.
11. The medium of claim 8 , wherein the device characteristic comprises a storage capacity.
A system and method for managing device characteristics in a computing environment involves monitoring and adjusting device parameters to optimize performance. The invention addresses the challenge of efficiently utilizing device resources, particularly storage capacity, to ensure reliable operation and prevent failures. The system includes a monitoring module that tracks device characteristics such as storage capacity, processing power, or memory usage. A control module dynamically adjusts these characteristics based on predefined thresholds or real-time conditions to maintain optimal performance. For storage capacity, the system may allocate, deallocate, or redistribute storage space to prevent overuse or underutilization. The invention also includes a reporting module that logs changes and alerts users or administrators when thresholds are exceeded. This ensures proactive management of device resources, reducing downtime and improving efficiency. The system is applicable to various computing devices, including servers, personal computers, and embedded systems, where resource management is critical. By dynamically adjusting storage capacity and other characteristics, the invention enhances system reliability and performance.
12. The medium of claim 8 , wherein a value of the device characteristic is different for at least two of the storage devices of the plurality of storage devices.
A system and method for managing storage devices in a computing environment addresses the challenge of optimizing performance and reliability in storage systems. The invention involves a plurality of storage devices, each having at least one adjustable device characteristic, such as read/write speed, power consumption, or error correction settings. The system includes a controller that dynamically adjusts these characteristics based on operational conditions, such as workload demands, environmental factors, or system health metrics. The controller monitors the storage devices and modifies their characteristics to balance performance, energy efficiency, and longevity. For example, during high-demand periods, the controller may increase the read/write speed of certain storage devices while reducing power consumption in others. The system also ensures that at least two storage devices in the plurality have different values for the same device characteristic, allowing for differentiated performance profiles tailored to specific tasks or priorities. This approach enhances overall system efficiency by adapting to varying workloads and environmental conditions, improving both performance and reliability. The invention is particularly useful in data centers, enterprise storage systems, and other environments where storage performance and energy efficiency are critical.
13. The medium of claim 8 , wherein the access pattern level corresponds to an access time range.
A system and method for optimizing data storage and retrieval in a distributed storage environment addresses the challenge of efficiently managing data access patterns to improve performance and resource utilization. The invention involves analyzing access patterns of data stored across multiple storage nodes to determine an access pattern level, which is then used to dynamically adjust storage and retrieval operations. The access pattern level corresponds to an access time range, indicating how frequently or recently the data is accessed. By categorizing data based on these access patterns, the system can prioritize storage locations, allocate resources, and optimize retrieval paths to reduce latency and improve efficiency. The method includes monitoring access requests, tracking access frequencies, and dynamically updating storage configurations based on the observed patterns. This approach ensures that frequently accessed data is stored in high-performance storage tiers, while less frequently accessed data is moved to lower-cost storage, balancing performance and cost. The system may also employ predictive algorithms to anticipate future access patterns and preemptively adjust storage configurations. The invention is particularly useful in large-scale distributed storage systems, such as cloud storage or enterprise data centers, where efficient data management is critical for performance and cost optimization.
14. The medium of claim 8 , wherein the access pattern level corresponds to an access count range.
A system and method for managing data access in a storage environment involves monitoring and controlling access patterns to optimize performance and security. The invention addresses the challenge of efficiently tracking and regulating data access to prevent unauthorized use, reduce latency, and improve resource allocation. The system categorizes access patterns based on predefined levels, where each level corresponds to a specific range of access counts. By associating access patterns with these levels, the system can dynamically adjust access permissions, prioritize data retrieval, or enforce security policies based on the frequency and type of access. This approach ensures that high-access data is handled efficiently while restricting or auditing excessive access attempts. The system may also integrate with existing storage infrastructure to provide real-time monitoring and adaptive responses to changing access demands. The invention enhances data management by balancing performance, security, and resource utilization in storage systems.
15. A system comprising: a non-transitory computer readable storage medium storing processor-executable computer program instructions, the instructions comprising instructions for: categorizing a plurality of data blocks into a plurality of categories according to an access pattern and a size corresponding to respective data blocks of the plurality of data blocks, wherein each category of the plurality of categories is associated with a respective access pattern level and respective data block storage size requirement; accessing device characteristic information for each storage device of a plurality of storage devices; determining a total number of data blocks in a first category; summing the device characteristic information to determine a total value of the plurality of storage devices; determining a proportional value for the first category for each of the plurality of storage devices by dividing the device characteristic value of each respective storage device by the total value; calculating a target number of data blocks for each storage device of the plurality of storage devices by multiplying the respective storage device's proportional value by the total number of data blocks in the first category, each respective storage device's calculated target number proportional to the respective storage device's device characteristic information; and redistributing the data blocks in the first category across the plurality of storage devices according to the calculated target number of data blocks for each of the plurality of storage devices; and a processor for executing the computer program instructions.
The system optimizes data distribution across multiple storage devices by categorizing data blocks based on access patterns and sizes. Each category is defined by an access pattern level and storage size requirement. The system retrieves device characteristics for each storage device, such as capacity or performance metrics, and calculates a proportional value for each device by dividing its characteristic value by the total sum of all device characteristics. For a selected category, the system determines the total number of data blocks and calculates a target number of blocks for each storage device by multiplying the proportional value by the total blocks in the category. This ensures data distribution aligns with device capabilities. The system then redistributes the data blocks across the storage devices according to the calculated targets, balancing load and performance. The processor executes these instructions to dynamically manage data placement, improving storage efficiency and access performance. The approach ensures that storage resources are utilized proportionally based on device characteristics, optimizing overall system performance.
16. The system of claim 15 , wherein the device characteristic comprises a determined performance characteristic.
A system for monitoring and analyzing device performance characteristics is disclosed. The system addresses the challenge of efficiently tracking and evaluating key performance metrics of electronic or mechanical devices to ensure optimal operation, maintenance, and reliability. The system includes a device interface module configured to collect real-time data from one or more devices, such as sensors, actuators, or processing units. The collected data is processed by an analysis module that extracts relevant performance characteristics, such as processing speed, energy consumption, or operational efficiency. These characteristics are then compared against predefined thresholds or historical data to identify deviations or trends. The system further includes a reporting module that generates alerts or reports based on the analysis, enabling proactive maintenance or performance optimization. Additionally, the system may incorporate machine learning algorithms to predict future performance trends or recommend corrective actions. The disclosed system enhances device reliability, reduces downtime, and improves overall system efficiency by continuously monitoring and analyzing performance metrics.
17. The system of claim 15 , wherein the device characteristic comprises a storage device's bandwidth.
A system for optimizing data storage performance monitors and adjusts storage device operations based on real-time characteristics. The system includes a monitoring module that tracks performance metrics of storage devices, such as bandwidth, latency, and error rates. A control module dynamically allocates data operations (e.g., read/write requests) across multiple storage devices to balance load and prevent bottlenecks. The system also includes a predictive module that forecasts future performance trends using historical data and adjusts configurations proactively. In one implementation, the system specifically evaluates the bandwidth of a storage device to determine optimal data distribution. By continuously analyzing these characteristics, the system ensures efficient resource utilization and minimizes performance degradation. The invention is particularly useful in high-demand environments where storage devices must handle large volumes of data with minimal latency. The system may integrate with existing storage architectures or operate as a standalone solution to enhance performance.
18. The system of claim 15 , wherein the device characteristic comprises a storage capacity.
A system for managing device characteristics in a networked environment addresses the challenge of efficiently monitoring and utilizing device capabilities to optimize performance and resource allocation. The system includes a networked device with a storage capacity characteristic, which is dynamically assessed and utilized to determine optimal data storage and processing tasks. The storage capacity is monitored in real-time to ensure efficient use of available resources, preventing overutilization or underutilization. The system may also include a controller that analyzes the storage capacity data to make decisions about data distribution, caching, or offloading tasks to other devices. By integrating storage capacity as a key characteristic, the system ensures that devices operate within their limits while maximizing overall network efficiency. This approach is particularly useful in distributed computing environments where devices with varying storage capacities must collaborate seamlessly. The system may also include additional device characteristics, such as processing power or network bandwidth, to further enhance performance optimization. The dynamic assessment and utilization of storage capacity enable adaptive resource management, improving system reliability and responsiveness.
19. The system of claim 15 , wherein a value of the device characteristic is different for at least two of the storage devices of the plurality of storage devices.
A system for managing storage devices in a data storage environment addresses the challenge of optimizing performance and reliability in heterogeneous storage systems. The system includes a plurality of storage devices, each with at least one device characteristic that influences performance, such as capacity, speed, or endurance. The system monitors and adjusts the allocation of data and operations across these storage devices based on their characteristics to improve efficiency and longevity. A key feature is that at least two storage devices in the system have different values for a given device characteristic, allowing the system to dynamically adapt to variations in device capabilities. This heterogeneity enables the system to balance workload distribution, prioritize critical operations, and mitigate wear on individual devices. The system may also include mechanisms for real-time assessment of device characteristics and automated reconfiguration of storage operations to maintain optimal performance. By leveraging differences in storage device properties, the system enhances overall system resilience and efficiency in diverse storage environments.
20. The system of claim 15 , wherein the access pattern level corresponds to an access time range.
A system for managing data access in a storage environment addresses the challenge of optimizing performance and resource utilization by dynamically adjusting access patterns based on time-based requirements. The system includes a storage controller that monitors and analyzes data access requests to determine access patterns, which are categorized into different levels corresponding to specific access time ranges. These levels define the expected latency or response time for data retrieval operations. The storage controller dynamically assigns access patterns to data objects based on their usage characteristics, such as frequency of access, priority, or application requirements. By correlating access patterns with time ranges, the system ensures that high-priority or time-sensitive data is retrieved within the specified latency thresholds, while less critical data may be handled with longer access times. The system may also include a data placement module that optimizes the physical storage location of data objects based on their assigned access patterns, improving overall system efficiency. Additionally, the system may incorporate predictive analytics to anticipate future access patterns and proactively adjust storage configurations to meet anticipated demand. This approach enhances performance, reduces latency, and ensures efficient resource allocation in storage systems.
21. The system of claim 15 , wherein the access pattern level corresponds to an access count range.
This invention relates to a data access control system designed to manage and monitor access patterns to digital resources. The system addresses the problem of unauthorized or excessive access to sensitive data by implementing a tiered access control mechanism based on predefined access pattern levels. Each access pattern level corresponds to a specific range of access counts, allowing administrators to define thresholds for normal, elevated, or suspicious access activity. When access attempts fall within a particular range, the system applies corresponding security measures, such as logging, alerts, or restrictions, to mitigate risks. The system dynamically adjusts access permissions based on real-time monitoring of access frequency, ensuring adaptive security without manual intervention. This approach enhances data protection by correlating access frequency with predefined security policies, reducing the likelihood of unauthorized data breaches or misuse. The invention is particularly useful in environments where access patterns vary and require granular control to maintain security and compliance.
Unknown
August 11, 2020
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